Multi-direct-drive wind power plant aggregation equivalence method and system suitable for oscillation characteristic analysis

Abstract

The invention discloses and belongs to the technical field of power system simulation modeling. The invention provides a multi-direct-drive wind power plant aggregation equivalence method suitable for oscillation characteristic analysis. The method comprises the following steps: merging multiple direct-drive wind power plants of a regional power grid; the key influence factors of oscillation characteristic analysis serve as clustering objects, the multiple direct-driven wind power plants merged under the same pooling station in the regional power grid are clustered, and a clustering set and a clustering center are generated; and during oscillation characteristic analysis, performing aggregation equivalence of the multiple direct-drive wind power plants based on the clustering set and the clustering center. According to the method, aggregation equivalence of the multiple direct-driven wind power plants suitable for oscillation characteristic analysis is realized, and analysis precision and simulation efficiency are both considered.

Description

Technical Field

[0001] The present invention relates to the technical field of power system simulation modeling, and more specifically, to a multi-direct-drive wind farm aggregation equivalent method suitable for oscillation characteristic analysis. Background Art

[0002] In recent years, my country's wind power, photovoltaic and other power electronic power generation equipment has entered a large-scale development stage, with the characteristics of high distribution concentration, prompting new energy sites to connect to the AC power system in a centralized grid-connected manner. In order to better analyze and suppress the sub / supersynchronous oscillation problems caused by the access of large-scale new energy sites, it is often necessary to build an electromagnetic transient model for simulation. If only a single wind farm is aggregated, there are still problems such as too many power electronic equipment and low simulation efficiency. Summary of the invention

[0003] In view of the above problems, the present invention proposes a multi-direct-drive wind farm aggregation equivalent method suitable for oscillation characteristic analysis, comprising:

[0004] Merge multiple direct-drive wind farms in regional power grids;

[0005] The key influencing factors of the oscillation characteristic analysis are used as clustering objects, and multiple direct-drive wind farms merged under the same collection station in the regional power grid are clustered to generate cluster sets and cluster centers;

[0006] When analyzing the oscillation characteristics, the aggregation and equivalence of multiple direct-drive wind farms are performed based on the cluster sets and cluster centers.

[0007] Optionally, multiple direct-drive wind farms in a regional grid can be merged, including:

[0008] Determine a direct-driven wind farm that is weakly coupled with the direct-driven wind farm to be studied and a direct-driven wind farm that is strongly coupled with the direct-driven wind farm to be studied in the regional power grid, and determine the electrical distance between the direct-driven wind farm that is weakly coupled with the direct-driven wind farm to be studied and the direct-driven wind farm that is strongly coupled with the direct-driven wind farm to be studied;

[0009] The direct-driven wind farms that are weakly coupled with the direct-driven wind farms to be studied are merged into the direct-driven wind farms that are strongly coupled with the direct-driven wind farms to be studied and are closest in electrical distance.

[0010] Optionally, merging the direct-driven wind farm that is weakly coupled with the direct-driven wind farm to be studied to the direct-driven wind farm that is strongly coupled with the direct-driven wind farm to be studied and has the closest electrical distance includes:

[0011] Based on the relative gain matrix theory, the direct-driven wind farms weakly coupled with the direct-driven wind farms to be studied are merged into the direct-driven wind farms strongly coupled with the direct-driven wind farms to be studied with the closest electrical distance in a capacity superposition manner.

[0012] Optionally, also include:

[0013] For the regional power grid, a system closed-loop transfer function including each new energy station is established;

[0014] Based on the closed-loop transfer function of the system, a dynamic coupling strength metric is constructed between each direct-drive wind farm and the direct-drive wind farm to be studied, and the dynamic coupling strength metric is compared with a preset threshold value. If the dynamic coupling strength metric is lower than the preset threshold value, the direct-drive wind farm corresponding to the dynamic coupling strength metric is weakly coupled with the direct-drive wind farm to be studied, otherwise it is strongly coupled.

[0015] Optional, key influencing factors, including:

[0016] The current inner loop proportional gain, the current inner loop integral time constant and the equivalent inductance of the wind farm line;

[0017] The equivalent inductance of the wind farm line includes: the line inductance sent to the collection station and the boost transformer leakage inductance.

[0018] Optionally, before clustering the multiple direct-drive wind farms merged under the same collection station in the regional power grid, a three-dimensional data sample set of the multiple direct-drive wind farms merged under the same collection station is constructed, each type of three-dimensional data sample in the three-dimensional data sample set is used as the initial clustering center, and the similarity between each type of three-dimensional data sample is calculated, and the support and attribution between each type of three-dimensional data sample are set to 0.

[0019] Optionally, based on the cluster set and the cluster center, performing aggregation and equalization of multiple direct-drive wind farms includes:

[0020] Based on the cluster set and cluster center, a direct-drive wind farm aggregation equivalent model is established to complete the aggregation equivalent of multiple direct-drive wind farms;

[0021] The capacity of the direct-driven wind farm aggregation equivalent model is the sum of the capacities of the direct-driven wind farms to be aggregated;

[0022] The model parameter values ​​of the direct-drive wind farm aggregation equivalent model are obtained by taking the corresponding coordinate values ​​of the cluster center.

[0023] On the other hand, the present invention also proposes a multi-direct-drive wind farm aggregation equivalent system suitable for oscillation characteristic analysis, comprising:

[0024] A merging unit, used for merging multiple direct-drive wind farms in a regional power grid;

[0025] A clustering unit is used to take the key influencing factors of the oscillation characteristic analysis as clustering objects, cluster the multiple direct-drive wind farms merged under the same collection station in the regional power grid, and generate cluster sets and cluster centers;

[0026] The equivalent unit is used to perform aggregation equivalent of multiple direct-drive wind farms based on the cluster set and the cluster center when analyzing the oscillation characteristics.

[0027] Optionally, the merging unit merges multiple direct-drive wind farms in the regional power grid, including:

[0028] Determine a direct-driven wind farm that is weakly coupled with the direct-driven wind farm to be studied and a direct-driven wind farm that is strongly coupled with the direct-driven wind farm to be studied in the regional power grid, and determine the electrical distance between the direct-driven wind farm that is weakly coupled with the direct-driven wind farm to be studied and the direct-driven wind farm that is strongly coupled with the direct-driven wind farm to be studied;

[0029] The direct-driven wind farms that are weakly coupled with the direct-driven wind farms to be studied are merged into the direct-driven wind farms that are strongly coupled with the direct-driven wind farms to be studied and are closest in electrical distance.

[0030] Optionally, the unit merges the direct-driven wind farm that is weakly coupled with the direct-driven wind farm to be studied into the direct-driven wind farm that is closest in electrical distance and strongly coupled with the direct-driven wind farm to be studied, including:

[0031] Based on the relative gain matrix theory, the direct-driven wind farms weakly coupled with the direct-driven wind farms to be studied are merged into the direct-driven wind farms strongly coupled with the direct-driven wind farms to be studied with the closest electrical distance in a capacity superposition manner.

[0032] Optionally, the merging unit is also used to:

[0033] For the regional power grid, a system closed-loop transfer function including each new energy station is established;

[0034] Based on the closed-loop transfer function of the system, a dynamic coupling strength metric is constructed between each direct-drive wind farm and the direct-drive wind farm to be studied, and the dynamic coupling strength metric is compared with a preset threshold value. If the dynamic coupling strength metric is lower than the preset threshold value, the direct-drive wind farm corresponding to the dynamic coupling strength metric is weakly coupled with the direct-drive wind farm to be studied, otherwise it is strongly coupled.

[0035] Optional, key influencing factors, including:

[0036] The current inner loop proportional gain, the current inner loop integral time constant and the equivalent inductance of the wind farm line;

[0037] The equivalent inductance of the wind farm line includes: the line inductance sent to the collection station and the boost transformer leakage inductance.

[0038] Optionally, the clustering unit is also used to:

[0039] A three-dimensional data sample set of multiple direct-drive wind farms merged under the same collection station is constructed, each type of three-dimensional data sample in the three-dimensional data sample set is used as the initial clustering center, and the similarity between each type of three-dimensional data sample is calculated, and the support and attribution between each type of three-dimensional data sample are set to 0.

[0040] Optionally, the equivalent unit performs aggregation equivalent of multiple direct-drive wind farms based on the cluster set and the cluster center, including:

[0041] Based on the cluster set and cluster center, a direct-drive wind farm aggregation equivalent model is established to complete the aggregation equivalent of multiple direct-drive wind farms;

[0042] The capacity of the direct-driven wind farm aggregation equivalent model is the sum of the capacities of the direct-driven wind farms to be aggregated;

[0043] The model parameter values ​​of the direct-drive wind farm aggregation equivalent model are obtained by taking the corresponding coordinate values ​​of the cluster center.

[0044] In yet another aspect, the present invention further provides a computing device, comprising: one or more processors;

[0045] a processor for executing one or more programs;

[0046] When the one or more programs are executed by the one or more processors, the above-described method is implemented.

[0047] In yet another aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed, the method described above is implemented.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] The present invention provides a method for aggregating equivalent values ​​of multiple direct-driven wind farms suitable for oscillation characteristic analysis, comprising: merging multiple direct-driven wind farms in a regional power grid; taking key influencing factors of oscillation characteristic analysis as clustering objects, clustering the merged multiple direct-driven wind farms under the same collection station in the regional power grid, generating cluster sets and cluster centers; and performing aggregation equivalent values ​​of multiple direct-driven wind farms based on the cluster sets and cluster centers during oscillation characteristic analysis. The present invention realizes the aggregation equivalent values ​​of multiple direct-driven wind farms suitable for oscillation characteristic analysis, and takes into account both analysis accuracy and simulation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 is a flow chart of the method of the present invention;

[0051] Figure 2 is a flow chart of an embodiment of the method of the present invention;

[0052] Figure 3 A topological diagram of a power grid in a certain area according to an embodiment of the method of the present invention;

[0053] Figure 4 This is a diagram of clustering results of multiple direct-drive wind farms under the aggregation station D based on the AP algorithm in an embodiment of the method of the present invention;

[0054] Figure 5 This is a comparison result diagram of impedance characteristic curves of the wind farm set {11, 13, 15} before and after aggregation equalization as viewed from the collection station D in the embodiment of the method of the present invention;

[0055] Figure 6 This is a graph of impedance characteristics before and after aggregation equivalent value of the system side as viewed from the grid-connected port of the wind farm set {11, 13, 15} (including the wind farm sets {10, 14, 17} and {12, 16}) in an embodiment of the method of the present invention;

[0056] Figure 7 It is a structural diagram of the system of the present invention. DETAILED DESCRIPTION

[0057] Now, exemplary embodiments of the present invention are described with reference to the accompanying drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. These embodiments are provided to disclose the present invention in detail and completely and to fully convey the scope of the present invention to those skilled in the art. The terms used in the exemplary embodiments shown in the accompanying drawings are not intended to limit the present invention. In the accompanying drawings, the same units / elements are marked with the same reference numerals.

[0058] Unless otherwise specified, the terms (including technical terms) used herein have the commonly understood meanings to those skilled in the art. In addition, it is understood that the terms defined in commonly used dictionaries should be understood to have the same meanings as those in the context of the relevant fields, and should not be understood as idealized or overly formal meanings.

[0059] Embodiment 1:

[0060] The present invention proposes a multi-direct-drive wind farm aggregation equivalent method suitable for oscillation characteristic analysis, such as Figure 1 As shown, including:

[0061] Step 1: Merge multiple direct-drive wind farms in the regional power grid;

[0062] Step 2: taking the key influencing factors of the oscillation characteristic analysis as clustering objects, clustering the multiple direct-drive wind farms merged under the same collection station in the regional power grid, and generating cluster sets and cluster centers;

[0063] Step 3: When analyzing the oscillation characteristics, based on the cluster set and the cluster center, perform aggregation and equivalence of multiple direct-drive wind farms.

[0064] Among them, multiple direct-drive wind farms in regional power grids are merged, including:

[0065] Determine a direct-driven wind farm that is weakly coupled with the direct-driven wind farm to be studied and a direct-driven wind farm that is strongly coupled with the direct-driven wind farm to be studied in the regional power grid, and determine the electrical distance between the direct-driven wind farm that is weakly coupled with the direct-driven wind farm to be studied and the direct-driven wind farm that is strongly coupled with the direct-driven wind farm to be studied;

[0066] The direct-driven wind farms that are weakly coupled with the direct-driven wind farms to be studied are merged into the direct-driven wind farms that are strongly coupled with the direct-driven wind farms to be studied and are closest in electrical distance.

[0067] The direct-driven wind farm that is weakly coupled with the direct-driven wind farm to be studied is merged into the direct-driven wind farm that is strongly coupled with the direct-driven wind farm to be studied and has the closest electrical distance, including:

[0068] Based on the relative gain matrix theory, the direct-driven wind farms weakly coupled with the direct-driven wind farms to be studied are merged into the direct-driven wind farms strongly coupled with the direct-driven wind farms to be studied with the closest electrical distance in a capacity superposition manner.

[0069] Among them, it also includes:

[0070] For the regional power grid, a system closed-loop transfer function including each new energy station is established;

[0071] Based on the closed-loop transfer function of the system, a dynamic coupling strength metric is constructed between each direct-drive wind farm and the direct-drive wind farm to be studied, and the dynamic coupling strength metric is compared with a preset threshold value. If the dynamic coupling strength metric is lower than the preset threshold value, the direct-drive wind farm corresponding to the dynamic coupling strength metric is weakly coupled with the direct-drive wind farm to be studied, otherwise it is strongly coupled.

[0072] Among them, the key influencing factors include:

[0073] The current inner loop proportional gain, the current inner loop integral time constant and the equivalent inductance of the wind farm line;

[0074] The equivalent inductance of the wind farm line includes: the line inductance sent to the collection station and the boost transformer leakage inductance.

[0075] Among them, before clustering the multiple direct-drive wind farms merged under the same collection station in the regional power grid, a three-dimensional data sample set of the multiple direct-drive wind farms merged under the same collection station is constructed, each type of three-dimensional data sample in the three-dimensional data sample set is used as the initial clustering center, and the similarity between each type of three-dimensional data sample is calculated, and the support and attribution between each type of three-dimensional data sample are set to 0.

[0076] Wherein, based on the cluster set and the cluster center, the aggregation and equivalent value of multiple direct-drive wind farms are performed, including:

[0077] Based on the cluster set and cluster center, a direct-drive wind farm aggregation equivalent model is established to complete the aggregation equivalent of multiple direct-drive wind farms;

[0078] The capacity of the direct-driven wind farm aggregation equivalent model is the sum of the capacities of the direct-driven wind farms to be aggregated;

[0079] The model parameter values ​​of the direct-drive wind farm aggregation equivalent model are obtained by taking the corresponding coordinate values ​​of the cluster center.

[0080] The present invention is further described below in conjunction with specific implementation cases:

[0081] Implementation steps: Figure 2 As shown, including:

[0082] (1) The relative gain array (RGA) theory is used to merge direct-drive wind farms to obtain a set of wind farms that are weakly coupled with the wind farm to be studied, and then they are merged into the wind farm that is strongly coupled with the site to be studied and has the closest electrical distance according to their capacity.

[0083] 1) For the regional power grid, a system closed-loop transfer function including each new energy station is formed;

[0084] 2) Constructing the dynamic coupling strength measurement RGA between direct-drive wind farms ij , for the wind farm i to be studied, obtain RGA ij Wind farm j below the threshold value;

[0085] Specifically:

[0086] For the closed-loop transfer function G, assume that the input u i Control output y j , consider the following two cases:

[0087] ① All other circuits are open: At this time there are:

[0088]

[0089] ② All other circuits are closed: At this time there are:

[0090]

[0091] Where: g ji =[G] ji represents the jith element of G; g′ji =1 / [G -1 ] ij Represents G -1 The inverse of the ij-th element of .

[0092] Enter u i With the output y j The relative gain between

[0093]

[0094] The RGA at any frequency can be calculated as follows:

[0095]

[0096] In the formula: Symbol Represents element-by-element multiplication (Hadamard product).

[0097] With |λ ij (s)| represents the relative gain between the input and output of a single unit. Considering the current multiplication module, the RGA matrix element that characterizes the gain between the input of a single unit and the output of the wind farm is:

[0098]

[0099] Where: S imax is the maximum capacity of all wind farms; s = j*2π*f, f is the frequency, and the calculation result under a single frequency can be used to approximate it, and f = 100 Hz can be taken.

[0100] 3) For the wind farm j that is weakly coupled with the station to be studied, it is merged into the wind farm that is strongly coupled with the station to be studied and has the closest electrical distance according to its capacity, and no detailed modeling is performed on wind farm j.

[0101] (2) For the direct-drive wind farms under the same collection station after merging, the key influencing factors in oscillation analysis (current inner loop proportional gain K p , current inner loop integration time constant T i and wind farm line equivalent inductance L Line , where L Line The line inductance and boost transformer leakage inductance sent to the collection station are taken as clustering objects, and multiple direct-drive wind farms are clustered based on the affinity propagation (AP) algorithm to form cluster sets and cluster centers.

[0102] 1) Construct a three-dimensional data sample set of each direct-drive wind farm under the same collection station {(K pm ,T im ,L Linem )}, taking each sample as the initial cluster center;

[0103] 2) Calculate each data sample (K pm ,T im ,L Linem ), the initial values ​​of support r and attribution a are set to 0;

[0104] Specifically:

[0105] Similarity s(m,n) indicates the similarity between point m and point n, which is the negative value of the Euclidean distance between the two points. The closer the distance between the two points, the greater the similarity value. Support r(m,n) is the message sent by point m to candidate class center point n, indicating the support degree of point m for point n to become its class center, that is, the possibility of point n becoming the class center of point m. Attribution a(m,n) is the message sent by candidate class center point n to data point m, indicating the suitability of point m to choose point n as the class center. s, r and a can be expressed as:

[0106] s(m,n)=-||x m -x n || 2 (6)

[0107]

[0108] 3) Iterate and calculate r and a until the clustering result remains unchanged or the number of iterations reaches the maximum value;

[0109] In order to avoid oscillation, a damping factor α∈(0,1) is introduced, which is:

[0110]

[0111] Where: t is the number of iterations.

[0112] 4) Calculate the decision matrix E and determine the cluster sets and cluster centers (K pn ,T in ,L Linen ).

[0113] For point m, the decision matrix E = {e(m,n)} can be used to judge its cluster center. If E(n,n)>0, then point n is the cluster center; for the remaining points, if E(n,l) is the maximum value of n rows, then point l is the cluster center of point n, and the calculation method is:

[0114]

[0115] (3) Establishing a direct-drive wind farm aggregation equivalent model for each cluster set;

[0116] The capacity of the aggregation equivalent model is the sum of the capacities of the wind farms to be aggregated.p 、T i and L Line The parameter is the cluster center (K pn ,T in ,L Linen ) and other parameters are typical values ​​under the same capacity.

[0117] like Figure 3 The figure shows a typical large-scale centralized access system for wind power. There are 17 wind farms in the north, most of which are direct-drive wind turbines with a total scale of about 4000MW. All of them are set as direct-drive wind turbines in the study. The wind farm nodes in the figure are named 1 to 17. The wind power is first collected to 220kV collection stations A to D, then sent to 500kV / 220kV substations a to i, and finally injected into the grid through 500kV substations j and k. Among them, wind farm 13 is a newly built wind farm to be studied.

[0118] (1) Adopting RGA theory to merge direct-drive wind farms;

[0119] against Figure 3 The regional power grid in the construction of the dynamic coupling strength measurement between the wind farm 13 to be studied and other wind farms, RGA ij Taking the maximum values ​​of the dd, dq, qd and qq axis components, the direct-drive wind farm merging results are shown in Table 1. The threshold value is set to 0.1, and wind farms 4 and 7 below the threshold value are merged into wind farms 5 and 8 with the closest electrical distance according to capacity.

[0120] Table 1

[0121]

[0122]

[0123] (2) Clustering of multiple direct-drive wind farms based on the AP algorithm;

[0124] For the direct-driven wind farms under the same collection station after merging, multiple direct-driven wind farms are clustered based on the AP algorithm. Taking collection station D as an example, the damping factor α is set to 0.5, and the clustering results are as follows: Figure 4 As shown in the figure, “○” indicates the wind farm to be clustered, “▽” indicates the cluster center, and “*” indicates different cluster sets. It can be seen that the 8 wind farms under the collection station D can be clustered into 3 wind farms, and the cluster sets are {10,14,17}, {11,13,15} and {12,16}. The corresponding cluster centers (K pn ,T in ,L Linen ) are (0.93, 0.04, 0.137), (2, 0.077, 0.168) and (1.48, 0.039, 0.145) respectively.

[0125] (3) Establishing a direct-drive wind farm aggregation equivalent model for each cluster set;

[0126] For the three wind farm clusters {10, 14, 17}, {11, 13, 15} and {12, 16} under the aggregation station D, the corresponding (K pn ,T in ,L Linen ) are the cluster center coordinates, which are (0.93, 0.04, 0.137), (2, 0.077, 0.168) and (1.48, 0.04, 0.145) respectively; their capacities are the sum of the capacities of the wind farms to be aggregated, which are 700MW, 900MW and 800MW respectively.

[0127] From the collection station D, the impedance characteristic curve comparison results of the wind farm set {11,13,15} before and after aggregation are as follows: Figure 5 As shown in Figure 1, the impedance characteristic curve of the system side before and after aggregation equalization from the grid-connected port of wind farm set {11,13,15} (including wind farm sets {10,14,17} and {12,16}) is as follows: Figure 6 shown.

[0128] The present invention can quantitatively characterize the dynamic coupling strength between wind farms based on factors such as the degree of electrical coupling between nodes, wind farm capacity and control characteristics, and merge the weakly coupled wind farms with the station to be studied into the strongly coupled wind farms with the closest electrical distance to the station to be studied; the three key influencing factors in the oscillation characteristic analysis, namely the current inner loop proportional gain, integral time constant and line equivalent inductance, are used as clustering objects, and the wind farm clustering set and aggregate equivalent model parameters are obtained through iteration. The calculation process is stable and there is no need to determine the number of clustering sets in advance. The technical solution of the present invention realizes the aggregation equivalent of multiple direct-drive wind farms suitable for oscillation characteristic analysis, taking into account both analysis accuracy and simulation efficiency, and provides a theoretical guidance for sub / super synchronous oscillation simulation modeling of new energy power grids in engineering practice.

[0129] Embodiment 2:

[0130] The present invention also proposes a multi-direct-drive wind farm aggregation equivalent system 200 suitable for oscillation characteristic analysis, such as Figure 7 As shown, including:

[0131] A merging unit 201, used for merging multiple direct-drive wind farms in a regional power grid;

[0132] The clustering unit 202 is used to take the key influencing factors of the oscillation characteristic analysis as clustering objects, cluster the multiple direct-drive wind farms merged under the same collection station in the regional power grid, and generate cluster sets and cluster centers;

[0133] The equalization unit 203 is used to perform aggregation equalization of multiple direct-drive wind farms based on the cluster set and the cluster center during oscillation characteristic analysis.

[0134] The merging unit 201 merges multiple direct-drive wind farms in the regional power grid, including:

[0135] Determine a direct-driven wind farm that is weakly coupled with the direct-driven wind farm to be studied and a direct-driven wind farm that is strongly coupled with the direct-driven wind farm to be studied in the regional power grid, and determine the electrical distance between the direct-driven wind farm that is weakly coupled with the direct-driven wind farm to be studied and the direct-driven wind farm that is strongly coupled with the direct-driven wind farm to be studied;

[0136] The direct-driven wind farms that are weakly coupled with the direct-driven wind farms to be studied are merged into the direct-driven wind farms that are strongly coupled with the direct-driven wind farms to be studied and are closest in electrical distance.

[0137] The merging unit merges the direct-driven wind farm that is weakly coupled with the direct-driven wind farm to be studied into the direct-driven wind farm that is closest in electrical distance and strongly coupled with the direct-driven wind farm to be studied, including:

[0138] Based on the relative gain matrix theory, the direct-driven wind farms weakly coupled with the direct-driven wind farms to be studied are merged into the direct-driven wind farms strongly coupled with the direct-driven wind farms to be studied with the closest electrical distance in a capacity superposition manner.

[0139] The merging unit 201 is also used for:

[0140] For the regional power grid, a system closed-loop transfer function including each new energy station is established;

[0141] Based on the closed-loop transfer function of the system, a dynamic coupling strength metric is constructed between each direct-drive wind farm and the direct-drive wind farm to be studied, and the dynamic coupling strength metric is compared with a preset threshold value. If the dynamic coupling strength metric is lower than the preset threshold value, the direct-drive wind farm corresponding to the dynamic coupling strength metric is weakly coupled with the direct-drive wind farm to be studied, otherwise it is strongly coupled.

[0142] Among them, the key influencing factors include:

[0143] The current inner loop proportional gain, the current inner loop integral time constant and the equivalent inductance of the wind farm line;

[0144] The wind farm line equivalent inductance includes: the line inductance sent to the collection station and the boost transformer leakage inductance.

[0145] The clustering unit 202 is further used for:

[0146] A three-dimensional data sample set of multiple direct-drive wind farms merged under the same collection station is constructed, each type of three-dimensional data sample in the three-dimensional data sample set is used as the initial clustering center, and the similarity between each type of three-dimensional data sample is calculated, and the support and attribution between each type of three-dimensional data sample are set to 0.

[0147] The equalization unit 203 performs aggregation equalization of multiple direct-drive wind farms based on the cluster set and the cluster center, including:

[0148] Based on the cluster set and cluster center, a direct-drive wind farm aggregation equivalent model is established to complete the aggregation equivalent of multiple direct-drive wind farms;

[0149] The capacity of the direct-driven wind farm aggregation equivalent model is the sum of the capacities of the direct-driven wind farms to be aggregated;

[0150] The model parameter values ​​of the direct-drive wind farm aggregation equivalent model are obtained by taking the corresponding coordinate values ​​of the cluster center.

[0151] The present invention realizes the aggregation equivalence of multiple direct-driven wind farms suitable for oscillation characteristic analysis, and takes into account both analysis accuracy and simulation efficiency.

[0152] Embodiment 3:

[0153] Based on the same inventive concept, the present invention also provides a computer device, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding functions, so as to implement the steps of the method in the above embodiment.

[0154] Embodiment 4:

[0155] Based on the same inventive concept, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here can include both a built-in storage medium in a computer device and an extended storage medium supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of the method in the above embodiment.

[0156] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The schemes in the embodiments of the present invention may be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0157] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0158] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0159] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0160] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0161] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A multi-direct-drive wind farm aggregation equivalent method suitable for oscillation characteristic analysis, characterized in that: include: Merge multiple direct-drive wind farms in regional power grids; The key influencing factors of the oscillation characteristic analysis are used as clustering objects, and multiple direct-drive wind farms merged under the same collection station in the regional power grid are clustered to generate cluster sets and cluster centers; When analyzing the oscillation characteristics, the aggregation and equivalence of multiple direct-drive wind farms are performed based on the cluster sets and cluster centers.

2. The method for equivalence of multiple direct-drive wind farms according to claim 1, characterized in that: The merging of multiple direct-drive wind farms for a regional power grid includes: Determine a direct-driven wind farm that is weakly coupled with the direct-driven wind farm to be studied and a direct-driven wind farm that is strongly coupled with the direct-driven wind farm to be studied in the regional power grid, and determine the electrical distance between the direct-driven wind farm that is weakly coupled with the direct-driven wind farm to be studied and the direct-driven wind farm that is strongly coupled with the direct-driven wind farm to be studied; The direct-driven wind farms that are weakly coupled with the direct-driven wind farms to be studied are merged into the direct-driven wind farms that are strongly coupled with the direct-driven wind farms to be studied and are closest in electrical distance.

3. The method for equivalence of multiple direct-drive wind farms according to claim 2, characterized in that: The step of merging the direct-driven wind farm weakly coupled with the direct-driven wind farm to be studied to the direct-driven wind farm strongly coupled with the direct-driven wind farm to be studied which is closest in electrical distance comprises: Based on the relative gain matrix theory, the direct-driven wind farms weakly coupled with the direct-driven wind farms to be studied are merged into the direct-driven wind farms strongly coupled with the direct-driven wind farms to be studied with the closest electrical distance in a capacity superposition manner.

4. The method for equivalence of multiple direct-drive wind farms according to claim 2, characterized in that: Also includes: For the regional power grid, a system closed-loop transfer function including each new energy station is established; Based on the closed-loop transfer function of the system, a dynamic coupling strength metric is constructed between each direct-drive wind farm and the direct-drive wind farm to be studied, and the dynamic coupling strength metric is compared with a preset threshold value. If the dynamic coupling strength metric is lower than the preset threshold value, the direct-drive wind farm corresponding to the dynamic coupling strength metric is weakly coupled with the direct-drive wind farm to be studied, otherwise it is strongly coupled.

5. The method for equivalence of multiple direct-drive wind farms according to claim 1, characterized in that: The key influencing factors include: The current inner loop proportional gain, the current inner loop integral time constant and the equivalent inductance of the wind farm line; The equivalent inductance of the wind farm line includes: the line inductance sent to the collection station and the boost transformer leakage inductance.

6. The method for equivalence of multiple direct-drive wind farms according to claim 1, characterized in that: Before clustering the multiple direct-drive wind farms merged under the same collection station in the regional power grid, a three-dimensional data sample set of the multiple direct-drive wind farms merged under the same collection station is constructed, each type of three-dimensional data sample in the three-dimensional data sample set is used as the initial clustering center, and the similarity between each type of three-dimensional data sample is calculated, and the support and attribution between each type of three-dimensional data sample are set to 0.

7. The method for equivalence of multiple direct-drive wind farms according to claim 1, characterized in that: The performing of aggregation and equalization of multiple direct-drive wind farms based on the cluster set and the cluster center includes: Based on the cluster set and cluster center, a direct-drive wind farm aggregation equivalent model is established to complete the aggregation equivalent of multiple direct-drive wind farms; The capacity of the direct-driven wind farm aggregation equivalent model is the sum of the capacities of the direct-driven wind farms to be aggregated; The model parameter values ​​of the direct-drive wind farm aggregation equivalent model are obtained by taking the corresponding coordinate values ​​of the cluster center.

8. A multi-direct-drive wind farm aggregation equivalent method suitable for oscillation characteristic analysis, characterized in that: include: A merging unit, used for merging multiple direct-drive wind farms in a regional power grid; A clustering unit is used to take the key influencing factors of the oscillation characteristic analysis as clustering objects, cluster the multiple direct-drive wind farms merged under the same collection station in the regional power grid, and generate cluster sets and cluster centers; The equivalent unit is used to perform aggregation equivalent of multiple direct-drive wind farms based on the cluster set and the cluster center when analyzing the oscillation characteristics.

9. The multi-direct-drive wind farm aggregation equivalent system according to claim 8 is characterized in that: The merging unit merges multiple direct-drive wind farms in a regional power grid, including: Determine a direct-driven wind farm that is weakly coupled with the direct-driven wind farm to be studied and a direct-driven wind farm that is strongly coupled with the direct-driven wind farm to be studied in the regional power grid, and determine the electrical distance between the direct-driven wind farm that is weakly coupled with the direct-driven wind farm to be studied and the direct-driven wind farm that is strongly coupled with the direct-driven wind farm to be studied; The direct-driven wind farms that are weakly coupled with the direct-driven wind farms to be studied are merged into the direct-driven wind farms that are strongly coupled with the direct-driven wind farms to be studied and are closest in electrical distance.

10. The method for equivalence of multiple direct-drive wind farms according to claim 9, characterized in that: The merging unit merges the direct-driven wind farm weakly coupled with the direct-driven wind farm to be studied into the direct-driven wind farm with the closest electrical distance and strong coupling with the direct-driven wind farm to be studied, including: Based on the relative gain matrix theory, the direct-driven wind farms weakly coupled with the direct-driven wind farms to be studied are merged into the direct-driven wind farms strongly coupled with the direct-driven wind farms to be studied with the closest electrical distance in a capacity superposition manner.

11. The method for equivalence of multiple direct-drive wind farms according to claim 9, characterized in that: The equivalent unit is also used for: For the regional power grid, a system closed-loop transfer function including each new energy station is established; Based on the closed-loop transfer function of the system, a dynamic coupling strength metric is constructed between each direct-drive wind farm and the direct-drive wind farm to be studied, and the dynamic coupling strength metric is compared with a preset threshold value. If the dynamic coupling strength metric is lower than the preset threshold value, the direct-drive wind farm corresponding to the dynamic coupling strength metric is weakly coupled with the direct-drive wind farm to be studied, otherwise it is strongly coupled.

12. The multi-direct-drive wind farm aggregation equivalent system according to claim 8, characterized in that: The key influencing factors include: The current inner loop proportional gain, the current inner loop integral time constant and the equivalent inductance of the wind farm line; The equivalent inductance of the wind farm line includes: the line inductance sent to the collection station and the boost transformer leakage inductance.

13. The multi-direct-drive wind farm aggregation equivalent system according to claim 8, characterized in that: The clustering unit is also used for: A three-dimensional data sample set of multiple direct-drive wind farms merged under the same collection station is constructed, each type of three-dimensional data sample in the three-dimensional data sample set is used as the initial clustering center, and the similarity between each type of three-dimensional data sample is calculated, and the support and attribution between each type of three-dimensional data sample are set to 0.

14. The multi-direct-drive wind farm aggregation equivalent system according to claim 8, characterized in that: The equivalent unit performs aggregation equivalent of multiple direct-drive wind farms based on the cluster set and the cluster center, including: Based on the cluster set and cluster center, a direct-drive wind farm aggregation equivalent model is established to complete the aggregation equivalent of multiple direct-drive wind farms; The capacity of the direct-driven wind farm aggregation equivalent model is the sum of the capacities of the direct-driven wind farms to be aggregated; The model parameter values ​​of the direct-drive wind farm aggregation equivalent model are obtained by taking the corresponding coordinate values ​​of the cluster center.

15. A computer device, characterized in that: include: one or more processors; a processor for executing one or more programs; When the one or more programs are executed by the one or more processors, the method according to any one of claims 1 to 7 is implemented.

16. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed, the method according to any one of claims 1 to 7 is implemented.

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